**1. Introduction**

Eectricity metering has undergone significant technological progress over the last 30 years, from electromechanical to electronic metering. An essential stage of this evolution arose with Automatic Meter Reading (AMR) [1], which includes the following main features [2,3]:


Moreover, the AMR was the basis for the succeeding evolution called Advanced Metering Infrastructure (AMI), which includes the following characteristics [4–6]:


AMI is related to the entire metering infrastructure and 'smart meter' is the popular name for the power metering device in this infrastructure. The term "smart" makes sense in the data processing approach, that is, the meter might process input data (voltage and current), transforming it into useful output information (e.g., energy consumption, power quality indicators, efficiency, and others). However, the concept of "smart" is not usually well defined, since most of the smart meters on the market do not have any smart functionality. Thus, there is a current demand for innovative and intelligent techniques to provide different sorts of information to utilities and consumers, improving their knowledge about energy use, efficiency, costs, consequently improving energy management. Therefore, researchers all over the world are proposing new tools and methods to provide further information about energy consumption [7,8], as well as proposing innovative ways to save energy [9–12].

In this context, a new generation of smart meters are called cognitive meters, which propose to use artificial intelligence and load disaggregation methods, also known as Non-Intrusive Load Monitoring (NILM). They recognize appliances connected to the grid during certain periods, while providing much more information to consumers than the traditional monthly consumption. Consequently, consumers' operations can be inspected to provide them with detailed information about their electrical consumption [13] so they can make better decisions concerning saving electricity, as well as implementing energy managemen<sup>t</sup> systems for automatic generation/consumption regulation. This is certainly a meaningful advance concerning the relationship between utilities and consumers [11–13]. Moreover, "c-meters" can provide advanced functionalities, such as detailed recommendation of when to use some particular appliances (according to statistical behavior, real-time consumption or hourly-energy price) and they can also sugges<sup>t</sup> some tip to save electricity over weeks, months and years.

Hart [14] initially introduced the NILM method, considering active power levels and distributing them into individual appliance data. With such a type of cognition, the consumer profile can be mapped and by using artificial intelligence techniques, new methodologies can be proposed for modern smart meters [7,8,10,14–22]. However, although NILM is quite a good approach to detect home appliances, it does not always present a reasonable accuracy, demanding other signal analysis or AI to improve detection accuracy.

Therefore, this paper introduces the Power Signature Blob (PSB)—a novel methodology that correlates a hybrid load disaggregation technique, which is based on feature extraction from current and voltage waveforms with power signatures. In this context, a predefined threshold level is compared to the active power variation, in addition to the power signatures. The procedure uses the difference between the actual active power and the last active power value used to define the step level direction—when the appliance is turned on or turned off. Considering that every step level detection is a new event, the novel NILM method calculates the proper features to classify the load and then, applies machine learning for appliance recognition. For classification, the NILM dataset from [15] was used, including instances of 35 appliances (In this paper, the term sample is defined as a signal acquisition from the voltage and current waveforms, and the term instance represents a dataset sample with the respective features.). Each appliance instance comprises active power and other power components (power factor, reactivity factor, and distortion factor). The features to classify and from the dataset are calculated using a contemporary power theory called Conservative Power Theory (CPT) [23,24]. Hence, the novelty of this paper is a new state-machine NILM that uses and acknowledges a dataset of 35 appliances with features based on power components by the CPT.

The next section discusses the main concept of load disaggregation and different techniques from the literature. Afterwards, the PSB method, using the power decompositions from CPT [23–25], is presented. Finally, simulations and experimental results depict the performance of the proposed approach.
